News
November 6, 2026
Inductive Places First in OpenADMET CYP Inhibition Challenge, Its Fourth Consecutive AI Drug Discovery Benchmark Win
NEW YORK, November 6, 2026
Inductive, the chemical intelligence organization for drug discovery, placed first in the OpenADMET CYP Direct Inhibition Blind Challenge, ranking ahead of more than 270 participants from leading biopharma, academic, and AI organizations. With its fourth consecutive first-place OpenADMET finish, Inductive remains undefeated in the world's largest blind ADMET prediction competitions.
Cytochrome P450 (CYP) enzymes metabolize the majority of small molecule drugs, and compounds that inhibit them can cause dangerous drug-drug interactions. Drug hunters need to identify this liability early and steer their designs away from it, and predictive models play an integral part in doing that.
The challenge asked participants to predict inhibition potency (pIC50) against four major isoforms (CYP3A4, CYP2D6, CYP2C9, and CYP1A2) for 750 compounds whose measurements were withheld until the challenge closed. The blind format, in which participants predict properties of previously unseen compounds, offers a rare chance to benchmark ADMET modeling under real-world conditions. The Direct Inhibition track drew over 270 eligible participants. Inductive placed first in a field of leading AI and biopharma teams that included NVIDIA (third) and Recursion (fourth).
Inductive previously placed first in the ASAP-Polaris-OpenADMET Antiviral Challenge (2025), the OpenADMET-ExpansionRx Challenge (February 2026), and the OpenADMET PXR Blind Challenge (July 2026). Across the four challenges, Inductive's Beacon models have been tested on 19 critical drug discovery endpoints and outperformed more than 1,000 competitors, including some of the largest biopharma and AI organizations in the world.
"For AI in chemistry to match the progress we've seen in coding and math, data is the bottleneck," said Ben Birnbaum, Inductive's Co-Founder and CTO. "Chemistry data is hard to come by because it comes from real experiments that are expensive and require weeks to months of expert work in chemistry labs. As a chemical intelligence organization, Inductive is built around assembling the largest chemistry datasets to drive AI for chemistry models towards superhuman levels of performance. This win is the latest proof point that this data yields highly predictive models for the drug hunters we work with.”
Inductive's state-of-the-art Beacon models are powered by its pre-competitive data consortium, one of the industry's largest and most diverse centralized ADMET datasets, and fine-tuned to each partner's chemical space.
"Our goal is to scale the impact of every scientist we work with," said Josh Haimson, Inductive's Co-Founder and CEO. "Pairing state-of-the-art models like these with Indy, our AI chemistry agent, helps each chemist make higher-quality decisions on every design, so programs need fewer design cycles to reach better development candidates."
About Inductive
Inductive is a chemical intelligence organization that embeds into drug programs and deploys frontier AI across experiment design and physical execution. Their chemical intelligence models, lab robotics, and expert chemists reason together about which experiments are scientifically valuable and which can be executed in the physical world, improving with every experiment. Inductive's models have placed first in four consecutive blind AI drug discovery benchmarks and support discovery programs with leading biopharma partners. For more information, please visit www.inductive.bio.
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- QC analytical reports (1H/13C/19F/2D NMR, chiral SFC)
- Propose, visualize, and triage forward and retrosyntheses
- Monitor the synthesis queue and flag stalled targets
- QC assay data as it lands and flag suspicious results
- Prioritize repeats and nominate compounds for downstream assays
- Analyze SAR and identify data gaps
- Evaluate IVIVC and suggest diagnostic experiments
- Design, enumerate, and triage analogs
- Launch and analyze physics-based workflows like docking and FEP
- Project human PK and efficacious dose
- Answer questions about a program’s history (what was learned and when)
- Track team progress against the target candidate profile
- Get new scientists up to speed on a program
- Make meeting-ready slides and figures
Prompt: I’m attaching two files with activity data, one with new data from today and one with historical data. Can you look through the new data and identify any QC issues that I should be aware of? Create a csv where every compound + assay run pair is labeled with any relevant issues.
Prompt: Do a nitrogen walk around the core of this molecule: O=C(NCC1=CC=C(C)C(F)=C1)C2=CC=C3C=C(N4[C@H](C)CCCC4)C=CN32
Prompt: Do an OH walk around this whole molecule: O=C(NCC1=CC=C(C)C(F)=C1)C2=CC=C3C=C(N4[C@H](C)CCCC4)C=CN32
Prompt: Replace the core of this molecule with this set of 5,6-ring systems (file attached): O=C(NCC1=CC=C(C)C(F)=C1)C2=CC=C3C=C(N4[C@H](C)CCCC4)C=CN32
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